Multiple object tracking has been one of the most challenging research topics in computer vision. Indeed, accurate multiple object tracking is the key element of video surveillance system where object counting and identification are the basis of determining when security violations within the area under surveillance are occurring.
Among the challenges in achieving accurate tracking in such systems are a number of phenomena. These phenomena include a) false detections, meaning that the system erroneously reports the presence of an object, e.g., a human being, at a particular location within the area under surveillance a particular time; b) missing data, meaning that the system has failed to detect the presence of an object in the area under surveillance that actually is there; c) occlusions, meaning that an object being tracked has “disappeared” behind another object being tracked or some fixed feature (e.g., column or partition) within the area under surveillance; d) irregular object motions, meaning, for example, that an object that was moving on a smooth trajectory has abruptly stopped or changed direction; e) changing appearances of the objects being tracked due, for example, to changed lighting conditions and/or the object presenting a different profile to the tracking camera.
Among the problems of determining when security violations have occurred or are occurring is the unavailability of electronic signals that could be profitably used in conjunction with the tracking algorithms. Such signals include, for example, signals generated when a door is opened or when an access device, such as a card reader, has been operated. Certainly an integrated system built “from the ground up” could easily be designed to incorporate such signaling, but it may not be practical or economically justifiable to provide such signals to the tracking system when the latter is added to a facility after the fact.